Assessing the Relative Risk of RVF Introduction to the USA via Airline Traffic

A few hundred years ago, just a blink in human history, disease was a complete mystery to mankind. Today, not only do we understand the causes of disease, we can predict how disease will disseminate through a population and where outbreaks will occur. This study aims to quantify the risk of introduction of Rift Valley Fever (RVF) into the USA. RVF is a multi-host mosquito borne disease which was first recognized in Kenya in 1931. Since then, RVF has been detected throughout the continent of Africa and, more recently, in Saudi Arabia. The virus has severe economic impact; it infects several different species of livestock, causing spontaneous abortions of fetuses and severe health detriments, including death, to adults. RVF can also infect humans, potentially causing hemorrhagic fever and death. In this study, we combine information about the life history of hosts, vectors, and pathogens with spatial distribution and movement data in order to quantify the potential of introduction into new areas. Specifically, we will analyze human, livestock, and mosquito movement data from RVF endemic areas in Africa to the USA and assess the relative risk in each mode of introduction, all while overlaying specific scenarios of known human behavior and travel.

Assessing the Relative Risk of RVF Introduction to the USA via Airline Traffic

A few hundred years ago, just a blink in human history, disease was a complete mystery to mankind. Today, not only do we understand the causes of disease, we can predict how disease will disseminate through a population and where outbreaks will occur. This study aims to quantify the risk of introduction of Rift Valley Fever (RVF) into the USA. RVF is a multi-host mosquito borne disease which was first recognized in Kenya in 1931. Since then, RVF has been detected throughout the continent of Africa and, more recently, in Saudi Arabia. The virus has severe economic impact; it infects several different species of livestock, causing spontaneous abortions of fetuses and severe health detriments, including death, to adults. RVF can also infect humans, potentially causing hemorrhagic fever and death. In this study, we combine information about the life history of hosts, vectors, and pathogens with spatial distribution and movement data in order to quantify the potential of introduction into new areas. Specifically, we will analyze human, livestock, and mosquito movement data from RVF endemic areas in Africa to the USA and assess the relative risk in each mode of introduction, all while overlaying specific scenarios of known human behavior and travel.

Thank you for your questions. First, our IGERT is named QSE3, which stands for Quantitative Spatial Ecology, Evolution, and Environment. Part of the QSE3’s overall goals include understanding the spread of emerging pathogens and the causes and consequences of shifting species distributions. Rift Valley Fever (RVF) is an emerging transcontinental disease and our research focuses on understanding how this disease can spread given different distributions of many relevant species, including livestock, mosquitoes, and humans, and their global connectivities.

Second, RVF has similarities to other multi-host vector borne diseases such as West Nile Virus and Lyme disease. Understanding the dynamics of pathogen movement in these systems is more challenging (as opposed to directly transmitted human diseases such as influenza or measles) because of the increased complexity. Consequently, by studying the RVF system we are also able to explore related dynamics for similar diseases to better understand them as well. For example, we are developing theoretical models that optimize sampling strategies for the early detection of multi host diseases.

Thank you for your questions. Our clients, the USDA, are the primary experts on the policy side of this issue. Our goal was to create models to inform such policy for the USDA if the need arises. I’m letting you know this as a disclaimer that none of us are experts on policy. That being said, we imagine that if need be the policies which would be implemented would be consistent with past screening measures, such as febrile surveillance and informing the public of risks.

We also expect that incorporating demographic information, such as traveler behavior, and temporal variation in our model would increase the specificity of a proposed surveillance scheme. For example, if certain behaviors make a traveler more likely to become infected with RVF, these travelers could be targeted for febrile surveillance. Similarly, since RVF outbreaks occur most commonly immediately after a drought period followed by a heavy rain, surveillance efforts may be best concentrated just after such events occur.

Thank you for your question. We have been testing several randomized models of disease endemicity in Africa and how this would change our prediction of where the disease might be introduced to the USA. Surprisingly (or maybe not so!), it looks like the largest predictor of introduction is sheer airflow to an airport, not environmental covariates in Africa such as population and rainfall. That is, we find similar qualitative results of where we might expect the disease to be introduced to the USA regardless of distribution of covariates in Africa due to the sheer volume of planes coming from Africa to JFK, LA, etc. These theoretical results are interesting in and of themselves and we are pursuing them further. Of course, in a real world situation, there may be a known outbreak localized to a certain location near an airport, and more attention might be paid to flights arriving to the USA from that airport.

For your second question- We have not completed any research on the specific impacts on policy. Our goal was to develop models that might inform the policy decisions of our clients, the USDA.

Hello Dr. Morse,
We are not policy-makers so are not familiar with limitations and guidelines necessary for implementing legal monitoring programs. Our main goal was to create models that may inform the policy decisions of our client, the USDA. However, our research suggests that focusing on specific kinds of travelers at specific times of the year may allow for increased monitoring efficiency with less effort. For example, tourists and individuals traveling for religious purposes may have higher exposure risk to RVF as they are more likely to enter high-risk rural areas. Also, because RVF outbreaks are known to occur seasonally and under specific hydrological conditions, preventive monitoring could be implemented based on traveler-specific predictors such as purpose of travel and locations visited.

Vincent. I really like your poster. Please tell me a little more about the method you are using to pull the assessment together. Is it a predetermined framework or a simulation model or other? Thanks, Gary

We used a variety of existing frameworks combined with our own unique analysis to determine the relative risk of RVF importation to airports in the US. We started with a map of endemic suitability for RVF in Africa (Clements et al., 2006) and a published model of airline flows (including up to three stops) from Africa to the US. (Huang et al., 2013) We then used the endemic suitability surface and known population densities (Afripop) to generate a map of relative risk of human RVF infection. Assuming that people are most likely to exit Africa via the closest airport, we formed Voronoi polygons around at each airport to partition the African continent. We took the population weighted mean risk in each of these polygons as a relative measure of risk of exportation from the airport contained in the polygon. Then, taking each African airport as a source of risk, we used the connectivity from the airline flows to determine the US airports at which passengers arrive. The relative risk of importation was then distributed to each of these US airports in a manner proportional to the relative airline flow volume to that airport from the “source” African airport. In this way, each US airport “gains” risk from each African airport to which it is connected. Taking all of the African airports into account gives us a total relative risk of importation to the US airports. We are now working to update this analysis with more detailed models of human movement and behavior based on travel survey data, as well as more more highly resolved temporal maps detailing the risk of epidemic outbreaks.

Hi Maggi,
Like in many mammals that can be infected with RVF, the virus tends to grow in population within the blood, and mosquitoes may carry the virus between hosts with subsequent feedings. There was a recent outbreak of Chikungunya, another mosquito-borne illness, in Italy that was thought to have been caused by an infected human arriving in Italy and being bitten by native mosquitoes- which spread the disease. Thanks for the question!

Interesting project. What is probability of human to human transmission vs human to cattle? Which are you modeling?

As cattle are at low density on the eastern seaboard, how does this affect your predictions?JFK and Dulles are entry points to the US. People often connect to other locations from those airports. Is this included in your risk analysis?

Hello Dr. Strauss,
Thank you!
The explicit probabilities of transmission from cattle-mosquito-cattle vs. human-mosquito-cattle are unknown- we are modeling the relative probability that an infected human leaves Africa based off of endemic suitability maps and human population maps in Africa and where they will land in the USA through airflow networks. For an in-depth explanation of our methods, please see our answer to Dr. Kofinas’s judge question.

This analysis was focused specifically on the risk of an introduction event as a result of human movement via the air travel network. For future work we are interested in exploring likelihood of disease invasion post-introduction, however, the part of our research presented in the video and poster does not attempt to model the forward transmission that could occur from an infected human arriving in the US and their subsequent interactions with mosquito vectors and/or cattle.

The airflow data that we used are the results of a published and validated spatial interaction model that takes into account connections in the airline network. The results we see in part reflect the large differences in the amount of traffic that arrives in the United States from Africa. For example, 71% of passengers arriving from the four most connected African countries and Saudi Arabia have flights terminating at JFK (Kasari et al., 2008). This of course does not preclude movement from a final airport destination into nearby areas by other transportation methods. We are working on applying models of human movement that could begin to account for these effects.